Clinical utility of genomic sequencing

Systematic reanalysis of genomic data by diagnostic laboratories: a scoping review of ethical, economic, legal and (psycho)social implications

With the introduction of Next Generation Sequencing (NGS) techniques increasing numbers of disease-associated variants are being identified. This ongoing progress might lead to diagnoses in formerly undiagnosed patients and novel insights in already solved cases. Therefore, many studies suggest introducing systematic reanalysis of NGS data in routine diagnostics. Introduction will, however, also have ethical, economic, legal and (psycho)social (ELSI) implications that Genetic Health Professionals (GHPs) from laboratories should consider before possible implementation of systematic reanalysis. To get a first impression we performed a scoping literature review. Our findings show that for the vast majority of included articles ELSI aspects were not mentioned as such. However, often these issues were raised implicitly. In total, we identified nine ELSI aspects, such as (perceived) professional responsibilities, implications for consent and cost-effectiveness. The identified ELSI aspects brought forward necessary trade-offs for GHPs to consciously take into account when considering responsible implementation of systematic reanalysis of NGS data in routine diagnostics, balancing the various strains on their laboratories and personnel while creating optimal results for new and former patients. Some important aspects are not well explored yet. For example, our study shows GHPs see the values of systematic reanalysis but also experience barriers, often mentioned as being practical or financial only, but in fact also being ethical or psychosocial. Engagement of these GHPs in further research on ELSI aspects is important for sustainable implementation.

Germline whole genome sequencing in adults with multiple primary tumors

Multiple primary tumors (MPTs) are a harbinger of hereditary cancer syndromes. Affected individuals often fit genetic testing criteria for a number of hereditary cancer genes and undergo multigene panel testing. Other genomic testing options, such as whole exome (WES) and whole genome sequencing (WGS) are available, but the utility of these genomic approaches as a second-tier test for those with uninformative multigene panel testing has not been explored. Here, we report our germline sequencing results from WGS in 9 patients with MPTs who had non-informative multigene panel testing. Following germline WGS, sequence (agnostic or 735 selected genes) and copy number variant (CNV) analysis was performed according to the American College of Medical Genetics (ACMG) standards and guidelines for interpreting sequence variants and reporting CNVs. In this cohort, WGS, as a second-tier test, did not identify additional pathogenic or likely pathogenic variants in cancer predisposition genes. Although we identified a CHEK2 likely pathogenic variant and a MUTYH pathogenic variant, both were previously identified in the multigene panels and were not explanatory for the presented type of tumors. CNV analysis also failed to identify any pathogenic or likely pathogenic variants in cancer predisposition genes. In summary, after multigene panel testing, WGS did not reveal any additional pathogenic variants in patients with MPTs. Our study, based on a small cohort of patients with MPT, suggests that germline gene panel testing may be sufficient to investigate these cases. Future studies with larger sample sizes may further elucidate the additional utility of WGS in MPTs.

Identification of potential feature genes in non-alcoholic fatty liver disease using bioinformatics analysis and machine learning strategies

The prevalence of non-alcoholic fatty liver disease (NAFLD) and NAFLD-associated hepatocellular carcinoma (HCC) has continuously increased in recent years. Machine learning is an effective method for screening the feature genes of a disease for prediction, prevention and personalized treatment. Here, we used the "limma" package and weighted gene co-expression network analysis (WGCNA) to screen 219 NAFLD-related genes and found that they were mainly enriched in inflammation-related pathways. Four feature genes (AXUD1, FOSB, GADD45B, and SOCS2) were screened by LASSO regression and support vector machine-recursive feature elimination (SVM-RFE) machine learning algorithms. Therefore, a clinical diagnostic model with an area under the curve (AUC) value of 0.994 was constructed, which was superior to other indicators of NAFLD. Significant correlations existed between feature genes expression and steatohepatitis histology or clinical variables. These findings were also validated in external datasets and a mouse model. Finally, we found that feature genes expression was significantly decreased in NAFLD-associated HCC and that SOCS2 may be a prognostic biomarker. Our findings may provide new insights into the diagnosis, prevention and treatment targets of NAFLD and NAFLD-associated HCC.

Systematic Collaborative Reanalysis of Genomic Data Improves Diagnostic Yield in Neurologic Rare Diseases

Many patients experiencing a rare disease remain undiagnosed even after genomic testing. Reanalysis of existing genomic data has shown to increase diagnostic yield, although there are few systematic and comprehensive reanalysis efforts that enable collaborative interpretation and future reinterpretation. The Undiagnosed Rare Disease Program of Catalonia project collated previously inconclusive good quality genomic data (panels, exomes, and genomes) and standardized phenotypic profiles from 323 families (543 individuals) with a neurologic rare disease. The data were reanalyzed systematically to identify relatedness, runs of homozygosity, consanguinity, single-nucleotide variants, insertions and deletions, and copy number variants. Data were shared and collaboratively interpreted within the consortium through a customized Genome-Phenome Analysis Platform, which also enables future data reinterpretation. Reanalysis of existing genomic data provided a diagnosis for 20.7% of the patients, including 1.8% diagnosed after the generation of additional genomic data to identify a second pathogenic heterozygous variant. Diagnostic rate was significantly higher for family-based exome/genome reanalysis compared with singleton panels. Most new diagnoses were attributable to recent gene-disease associations (50.8%), additional or improved bioinformatic analysis (19.7%), and standardized phenotyping data integrated within the Undiagnosed Rare Disease Program of Catalonia Genome-Phenome Analysis Platform functionalities (18%).

Utility of Gene Panels for the Diagnosis of Inborn Errors of Metabolism in a Metabolic Reference Center

Next-generation sequencing (NGS) technologies have been proposed as a first-line test for the diagnosis of inborn errors of metabolism (IEM), a group of genetically heterogeneous disorders with overlapping or nonspecific phenotypes. Over a 3-year period, we prospectively analyzed 311 pediatric patients with a suspected IEM using four targeted gene panels. The rate of positive diagnosis was 61.86% for intermediary metabolism defects, 32.84% for complex molecular defects, 19% for hypoglycemic/hyperglycemic events, and 17% for mitochondrial diseases, and a conclusive molecular diagnosis was established in 2-4 weeks. Forty-one patients for whom negative results were obtained with the mitochondrial diseases panel underwent subsequent analyses using the NeuroSeq panel, which groups all genes from the individual panels together with genes associated with neurological disorders (1870 genes in total). This achieved a diagnostic rate of 32%. We next evaluated the utility of a tool, Phenomizer, for differential diagnosis, and established a correlation between phenotype and molecular findings in 39.3% of patients. Finally, we evaluated the mutational architecture of the genes analyzed by determining z-scores, loss-of-function observed/expected upper bound fraction (LOEUF), and haploinsufficiency (HI) scores. In summary, targeted gene panels for specific groups of IEMs enabled rapid and effective diagnosis, which is critical for the therapeutic management of IEM patients.

Immuno-Hormonal, Genetic and Metabolic Profiling of Newborns as a Basis for the Life-Long OneHealth Medical Record: A Scoping Review

Holistic and life-long medical surveillance is the core of personalised medicine and supports an optimal implementation of both preventive and curative healthcare. Personal medical records are only partially unified by hospital or general practitioner informatics systems, but only for citizens with long-term permanent residence. Otherwise, insight into the medical history of patients greatly depends on their medical archive and memory. Additionally, occupational exposure records are not combined with clinical or general practitioner records. Environmental exposure starts preconceptionally and continues during pregnancy by transplacental exposure. Antenatal exposure is partially dependent on parental lifestyle, residence and occupation. Newborn screening (NBS) is currently being performed in developed countries and includes testing for rare genetic, hormone-related, and metabolic conditions. Transplacental exposure to substances such as endocrine disruptors, air pollutants and drugs may have life-long health consequences. However, despite the recognised impact of transplacental exposure on the increased risk of metabolic syndrome, neurobehavioral disorders as well as immunodisturbances including allergy and infertility, not a single test within NBS is geared toward detecting biomarkers of exposure (xenobiotics or their metabolites, nutrients) or effect such as oestradiol, testosterone and cytokines, known for being associated with various health risks and disturbed by transplacental xenobiotic exposures. The outcomes of ongoing exposome projects might be exploited to this purpose. Developing and using a OneHealth Medical Record (OneHealthMR) may allow the incorporated chip to harvest information from different sources, with high integration added value for health prevention and care: environmental exposures, occupational health records as well as diagnostics of chronic diseases, allergies and medication usages, from birth and throughout life. Such a concept may present legal and ethical issues pertaining to personal data protection, requiring no significant investments and exploits available technologies and algorithms, putting emphasis on the prevention and integration of environmental exposure and health data.

Improving interpretation of genetic testing for hereditary hemorrhagic, thrombotic, and platelet disorders

The last 10 years have seen an explosion in the amount of data available through next-generation sequencing. These data are advancing quickly, and this pace makes it difficult for most practitioners to easily keep up with all of the new information. Complicating this understanding is sometimes conflicting information about variant pathogenicity or even about the role of some genes in the pathogenesis of disease. The more widespread clinical use of sequencing has expanded phenotypes, including the identification of mild phenotypes associated with previously serious disease, such as with some variants in RUNX1, MYH9, ITG2A, and others. Several organizations have taken up the task of cataloging and systematically evaluating genes and variants using a standardized approach and making the data publicly available so that others can benefit from their gene/variant curation. The efforts in testing for hereditary hemorrhagic, thrombotic, and platelet disorders have been led by the International Society on Thrombosis and Haemostasis Scientific Standardization Committee on Genomics in Thrombosis and Hemostasis, the American Society of Hematology, and the National Institutes of Health National Human Genome Research Institute Clinical Genome Resource. This article outlines current efforts to improve the interpretation of genetic testing and the role of standardizing and disseminating information. By assessing the strength of gene-disease associations, standardizing variant curation guidelines, sharing genomic data among expert members, and incorporating data from existing disease databases, the number of variants of uncertain significance will decrease, thereby improving the value of genetic testing as a diagnostic tool.

Ocular coloboma: Genetic variants reveal a dynamic model of eye development

Ocular coloboma is a congenital disorder of the eye where a gap exists in the inferior retina, lens, iris, or optic nerve tissue. With a prevalence of 2-19 per 100,000 live births, coloboma, and microphthalmia, an associated ocular disorder, represent up to 10% of childhood blindness. It manifests due to the failure of choroid fissure closure during eye development, and it is a part of a spectrum of ocular disorders that include microphthalmia and anophthalmia. Use of genetic approaches from classical pedigree analyses to next generation sequencing has identified more than 40 loci that are associated with the causality of ocular coloboma. As we have expanded studies to include singleton cases, hereditability has been very challenging to prove. As such, researchers over the past 20 years, have unraveled the complex interrelationship amongst these 40 genes using vertebrate model organisms. Such research has greatly increased our understanding of eye development. These genes function to regulate initial specification of the eye field, migration of retinal precursors, patterning of the retina, neural crest cell biology, and activity of head mesoderm. This review will discuss the discovery of loci using patient data, their investigations in animal models, and the recent advances stemming from animal models that shed new light in patient diagnosis.

Rapid Phenotype-Driven Gene Sequencing with the NeoSeq Panel: A Diagnostic Tool for Critically Ill Newborns with Suspected Genetic Disease

New genomic sequencing techniques have shown considerable promise in the field of neonatology, increasing the diagnostic rate and reducing time to diagnosis. However, several obstacles have hindered the incorporation of this technology into routine clinical practice. We prospectively evaluated the diagnostic rate and diagnostic turnaround time achieved in newborns with suspected genetic diseases using a rapid phenotype-driven gene panel (NeoSeq) containing 1870 genes implicated in congenital malformations and neurological and metabolic disorders of early onset (<2 months of age). Of the 33 newborns recruited, a genomic diagnosis was established for 13 (39.4%) patients (median diagnostic turnaround time, 7.5 days), resulting in clinical management changes in 10 (76.9%) patients. An analysis of 12 previous prospective massive sequencing studies (whole genome (WGS), whole exome (WES), and clinical exome (CES) sequencing) in newborns admitted to neonatal intensive care units (NICUs) with suspected genetic disorders revealed a comparable median diagnostic rate (37.2%), but a higher median diagnostic turnaround time (22.3 days) than that obtained with NeoSeq. Our phenotype-driven gene panel, which is specific for genetic diseases in critically ill newborns is an affordable alternative to WGS and WES that offers comparable diagnostic efficacy, supporting its implementation as a first-tier genetic test in NICUs.